bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1

1 Full title: Landscape structure influences avian diversity in tropical urban mosaics 2 Short title: Avian species diversity

3

4 Authors 5 Trymore Muderere1*. Amon Murwira1. Paradzayi Tagwireyi1. Ngoni Chiweshe2 6 Corresponding author* Trymore Muderere, Phone +263-775-031-477, Email: 7 [email protected] 8 1Department of Geography and Environmental Science, Centre for Geoinformation Science and 9 Earth Observation, University of , P.O Box MP167, Mount Pleasant, Harare, 10 Zimbabwe 11 2Department of Tropical Resource Ecology, University of Zimbabwe, P.O Box MP167, Mount 12 Pleasant, Harare, Zimbabwe 13 14 Abstract 15 In this study, we tested whether urban landscape structure influences avian species diversity 16 using data for Harare, Zimbabwe. Initially, we quantified landscape structure using 17 fragmentation indices derived from a 5m resolution SPOT 5 imagery. We collected species 18 data through field-based observations of at 35 locations occurring in five land use/land 19 cover types. We quantified avian species diversity using Barger-Parker, Menhinick and 20 Simpson’s Indices. Regression analysis was used to determine the nature and strength of the 21 relationships between avian species diversity and fragmentation indices. Results indicated that 22 woodland specialist avian species are negatively associated with landscape fragmentation, while 23 grassland specialist and generalist avian species positively responded to patch edge density, 24 patch size and shape complexity. Overall, our results suggest that changes in landscape 25 structure due to expansion of built-up areas in tropical urban areas may influence avian species 26 diversity. 27 28 Keywords: landscape fragmentation, SPOT 5, avian species diversity, urban landscape ecology bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 2

29 Introduction 30 Understanding the factors that influence biodiversity within urban landscapes is fundamental to 31 the planning and development of biodiversity tolerant cities. In the 21st Century, increasing 32 landscape fragmentation resulting from urban development and transportation infrastructure is 33 considered a predominant driver of biodiversity loss in tropical ecosystems [1]. Urban 34 development has a marked impact on the environment [2] as it replaces wildlife habitat with 35 artificial surfaces that are unsuitable as wildlife habitat e.g., asphalt surfaces [3]. Although urban 36 areas occupy <3% of the Earth’s land surface area [4], their ecological impacts span over large 37 spatial extents and sometimes beyond the urban boundaries [5]. Thus, understanding biological 38 diversity-landscape structure (spatial configuration of a given land cover class) relationships is 39 increasingly becoming critical in urban planning [6]. In urban areas, the expansion of built-up 40 areas as well as its configuration is hypothesised to have differential but significant impacts on 41 biodiversity patterns [3], thereby making objective methods for quantifying this phenomena 42 critical. 43 44 The quantification of landscape structure in urban landscapes is an important step towards 45 developing urban growth management plans that promote biological diversity. Thus, the 46 development of methods for understanding the impact of urban development on biological 47 diversity in the is critical for biodiversity conservation and enhancement of wildlife 48 persistence in these ecosystems. Such methods may need to focus on improving the estimates of 49 landscape structure-biodiversity relationships. Although field measurements are regarded as the 50 most accurate method of quantifying landscape structure-biodiversity relationships, these 51 measurements are costly and labour intensive and can only be feasible over smaller scales [7, 8]. 52 In this regard the development of methods that supplement field measurements is important. 53 54 Developments in Geographic Information Systems (GIS) and satellite remote sensing have made 55 it possible to quantify landscape structure rapidly [2, 3]. In the past, several studies have 56 demonstrated the utility of landscape indices derived from satellite remotely sensed GIS data in 57 estimating landscape-biodiversity relationships across various spatiotemporal scales in temperate 58 landscapes [9-11]. For example, in a study by Coops et al. [12] satellite-derived landscape 59 metrics were used to predict bird species richness in Ontario, Canada using the Moderate- 60 resolution Imaging Spectroradiometer (MODIS) and explained variance ranging between 47 to 61 75%. Similarly, Guo et al. [10] used a coarse Landsat Thematic Mapper (TM) to estimate avian 62 species habitat relationships in temperate landscapes of Saskatchewan, Canada and their highest 63 coefficient of determination (R2) was 53%. Wood et al. [11] compared remotely sensed and field- 64 measured vegetation structure in predicting avian species density in Wisconsin, USA and 65 observed that air photo (R2 = 0.54) and Landsat TM satellite image (R2 = 0.52) were better 66 predictors of avian species density than field-measured vegetation structure (R2 = 0.32). In urban 67 landscapes, relatively higher resolution imagery could be of use in modelling the relationship 68 between landscape structure and biodiversity. 69 70 The availability of high spatial resolution sensors such as SPOT 5 has provided data that could 71 be used to improve the quantification and mapping of landscape structure indices in urban 72 landscapes that in turn may allow for improved understanding of landscape structure-biodiversity 73 relationships. To date, studies that assess the utility of high spatial resolution multispectral bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 3

74 imagery such as SPOT 5 in estimating landscape structure-biodiversity relationships in tropical 75 urban ecosystems remains rudimentary. 76 77 In this study, we tested whether and in what way landscape structure indices derived from 78 remotely sensed land cover relate with avian species diversity patterns in Harare, Zimbabwe. 79 Specifically, we tested whether and to what extent avian species diversity respond to constraints 80 including habitat patch size, habitat shape complexity, and habitat inter-patch distance. We 81 derived bird species data from field surveys and landscape structure data from high spatial 82 resolution sensors, i.e. SPOT 5 for Harare, Zimbabwe. We expect differential responses of avian 83 species diversity to habitat constraints. For example, woodland and grassland specialist avian 84 species may be negatively related to decrease in habitat patch size, increased shape complexity 85 and habitat isolation distance. While generalist species will respond positively to changes in 86 habitat conditions. 87 88 89 Materials and Methods 90 Study area 91 The study was carried out in the Harare Metropolitan province of Zimbabwe (Figure 1). The 92 Harare metropolitan area is approximately 892km2 in spatial extent and has a human population 93 of approximately 2.5 million [13]. The center of the study area, is located at Longitude 31º7ꞌE 94 and Latitude 17º55ꞌS with an altitude range of 1400-1500m above sea level. The city experience 95 two distinct seasons i.e., hot wet summers (October – April) and cool dry winters (May – 96 September). The mean annual rainfall ranges between 800-1000mm, while mean annual 97 temperature ranges between 25 – 27 ºC [14]. 98 99 #Insert Figure 1 100 101 Our own fieldwork showed that the prevalent land use/land cover (LULC) types in the city 102 include grasslands/pasture and cropland (64.0%), forested (21.0%), urban built-up areas (10.7%), 103 bare ground (3.8%) and water (0.5%). The forested land cover is mainly deciduous dry 104 woodland dominated by spiciformis, Julbernardia globiflora and Uapaca 105 kirkiana [15]. The bare ground cover type consists of exposed surfaces and area under active 106 urban development. The water cover type includes impoundments and rivers. The urban built-up 107 area is made up of impervious surface covering including road networks, industrial areas, high 108 and low density residential areas. The study site was selected because it represents an ideal 109 location to study landscape structure-biodiversity relationships in the context of regional and 110 urban planning. The area is currently undergoing a rapid increase in human population associated 111 with unguided urban development patterns whose impacts have not been quantified. 112 113 Quantifying landscape structure 114 We derived landscape structure data from a 5-m spatial resolution SPOT 5 image of Harare. 115 Specifically, using Object Based Image Analysis (OBIA) in Trimble eCognition (Trimble, 116 Munich, German) on a desktop computer, we obtained discrete landscape classes of habitat 117 patches for avian i.e., (1) forested areas, (2) grasslands as well as (3) built up areas. Overall 118 mapping accuracy was 89.7%, Kappa coefficient of 84.3% based on 340 sampling test points. 119 We used the Effective Mesh Size [16, 17], grid mesh (mesh size = 4000m2) to characterize the bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 4

120 landscape structure in the study area. The 4000m2 mesh size was used because this represents the 121 average home range size of typical urban birds [9, 16, 18-21]. We then used the Patch Analyst 122 tool [22] in ArcGIS 10.2 (Environmental Systems Research Institute, Redlands, California, 123 USA) following Tagwireyi and Sullivan [23] to quantify landscape structure (configuration and 124 composition) based on 16 landscape patch metrics default in the Patch Analyst tool and 2 125 Effective Mesh Size landscape patch metrics default in the Effective Mesh Size tool [16, 17]. We 126 tested the 18 patch metrics for multi-collinearity with pairwise Pearson’s correlation [24] and 127 removed all metrics with R2 > 0.90 from further analysis following Graham [25]. Patch metrics 128 were highly variable across landscape classes (SI 1). 129 130 Sampling design 131 In a GIS, we processed the study area into a LULC categories layer representing three LULC 132 types i.e., low urbanization grasslands, low urbanization forested area and built-up areas (Table 133 1). Subcategories were defined for each category to account for variations each context 134 presented. Altogether we had seven LULC subcategories and representing three LULC types and 135 35 transect sampling sites (Table 1). 136 137 #Insert Table 1 138 139 Using the LULC categories base map and the Random Sampling Tool in Quantum GIS 2.6.1 140 (QGIS Development Team, Switzerland) we stratified the study area (excluding private and 141 security areas e.g., military and airport land) into five sampling sites for each LULC subcategory 142 (total 35 sites) (Table 1). We deemed the sample of 35 sites representative for statistical purposes 143 following Rawlings et al. [24]. Each of the points was used as the center of the 600m transect 144 lines along which we surveyed the birds. The sampling sites were positioned at least 1.5 145 kilometers apart to ensure spatial independence between surveyed avian species and on different 146 land cover types to account for habitat variation within sites [26]. 147 148 Avian species surveys 149 At each sampling site we recorded observations of diurnal-active birds using an effective 150 detection distance of 50m [27] along either side of the 600m sampling lines. The surveys were 151 done at four different times of the day i.e. between: 6am-9am; 9am-12pm; 12pm-3pm; 3pm-6pm 152 during the summer months of February and April 2015 (hot-), to account for 153 differences in avian species behavior on different times of the day [20, 28]. On each visit, the 154 same observers waited for about five minutes to allow avian species to resume normal activity 155 following MacArthur and MacArthur [28] and then recorded all avian species seen patched, 156 flying or foraging within a 50m distance from the 600m transect line (see SI 1). We identified the 157 birds to species level based on expert knowledge and a field guide book i.e., Roberts Birds of 158 Southern Africa [29]. We also categorized avian species into three ecological guilds (generalists, 159 woodland specialists as well as grassland specialists) because we investigated landscape 160 influence on the birds at guild level. 161 Avian species were selected as the model species, because they are highly mobile and can 162 respond to landscape change quickly than ground dwelling mammals or other rarely seen species 163 [9] which makes birds useful indicators of species responses to urban development induced 164 environmental change. The study focused on overall avian species than select target species, 165 common in many studies [30]. The advantage of focusing on overall avian species is that it bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 5

166 allows the study to account for avian species with different life histories and behaviors [18, 30, 167 31]. 168 169 Quantifying avian species diversity 170 We used the Menhinick, Berger-Parker and Simpson’s indices to quantify avian species diversity 171 [9, 32, 33] (Table 2). A diversity index is a mathematical measure of biodiversity providing 172 important information about rarity and commonness of a species in a community [33]. We 173 calculated Menhinick’s Index as 1-(S/√N) where N= the number of individuals in a sample and S 174 = the number of species recorded [34]. The Berger-Parker Index was estimated as 1-Nmax/N, 175 where Nmax is the number of individuals in the most abundant species and N is the total number 2 2 176 of individuals in a sample [32]. The Simpson’s Index was estimated as 1-∑P i, where P i is the 177 total number of organisms of each particular species from the total number of organisms of all 178 species [33]. We chose the Menhinick, Berger-Parker and Simpson’s Indices because they are 179 spatially and temporarily stable, robust and biologically intuitive measures of biodiversity [34], 180 although they remain susceptible to sampling size [33, 35]. We applied the reciprocal 1-D to the 181 indices so that an increase in the index accompanies an increase in diversity for ease of intuitive 182 interpretation following Whittaker [36] and Magurran [34]. 183 184 Relating landscape fragmentation indices to avian species diversity 185 Prior to regression analysis we tested the avian species data for normality using the Kolmogorov- 186 Smirnov test to test [37] for conformity to the simple regression assumption for randomness and 187 we found a normal distribution (p>0.05). We then used simple regression analysis to examine the 188 direction and strength of the relationship between fragmentation indices (independent variables) 189 and avian species diversity (dependent variables) in MS Excel and Statistical Package for Social 190 Science Version 18 [38]. The strength of each regression model was evaluated based on the 191 coefficient of determination (R2) and the level of significance (p-value). 192 193 Results 194 Avian species diversity-landscape structure relationships 195 We surveyed 6081 birds representing 69 species in 35, 600m transects. Thirty percent of the 196 surveyed birds were observed in low urbanization grassland habitat, 46% in built-up areas and 197 24% in low urbanization forested land. We also observed that bird species abundance, richness 198 and diversity (i.e., Menhinick’s, Berger-Parker and Simpson’s Indices) varies across the three 199 LULC classes (Table 2). 200 201 #Insert Table 2 202 203 Woodland specialist avian species - landscape structure relationships 204 Simple regression showed that woodland specialist avian species were negativity associated with 205 patch metrics derived from low urbanization forested cover type, specifically shape complexity 206 (R2 = 0.635), shape size (R2 = 0.616) and isolation distance (R2 = 0.778) (Figure 2). 207 208 #Insert Figure 2(a),(b),(c). 209 210 Grassland specialist avian species - landscape structure relationships bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 6

211 Simple regression showed that grassland specialist avian species had a strong positive 212 polynomial relationship with patch edge derived from low urbanization grassland cover type (R2 213 = 473, Figure 3) and not significant (p>0.05) association with patch size and isolation distance. 214 215 #Insert Figure 3 216 217 Generalist avian species - landscape structure relationships 218 Simple regression showed significant (p <0.05) positive regression between generalist avian 219 species and habitat fragment size, shape complexity (R2 = 0.553, R2 = 0.728) (Figure 4) but not 220 significant relationship with isolation distance of the intensely built-up cover type. 221 222 #Insert Figure 4(a),(b) 223 224 Discussion 225 Results of this study indicate that landscape structure elements influence avian species diversity 226 in the study area. These results are consistent with our initial hypothesis that landscape structure 227 influences avian species diversity in urban landscapes. The results are also consistent with 228 findings of previous studies in urban and non-urban landscapes of North America [e.g.,39], 229 Central Europe [e.g.,18] and Australia [e.g., 40] who observed that landscape constraints 230 operating at habitat level influence avian species diversity. 231 232 Results also indicated that avian species diversity of woodland specialists negatively correlated 233 with edge density of the low urbanization forested cover type, suggesting that for these specialist 234 avian species increased fragmentation in woodlands due to urban development has negative 235 impacts on them. This is not surprising as McWilliam and Brown [39] also observed similar 236 responses in Ontario, Canada where a decrease in forest cover size accompanied by an increase 237 in the size of built-up area caused decline in the diversity of forest interior specialist species over 238 a ten-year period. Rodwald and Yahner [41] also observed linkages between landscape 239 composition and avian community structure in central Pennsylvania, USA. However, the result is 240 significant in informing urban planning practices that may need to preserve woodland specialist 241 species. We therefore deduce that landscape metrics derived from high resolution imagery can be 242 used for accurate estimation of avian species diversity in urban landscapes. In contrast, but not 243 surprising, results also indicated that grassland specialist avian species positively correlated well 244 with habitat shape complexity especially high edge effects. These results suggest that while 245 woodland specialists are negatively affected by woodland fragmentation, this process facilitates 246 grassland avian species expansion. Again this result is consistent with Jones and Bock [42] who 247 reported that open spaces typically low urbanization grassland areas can sustain a high diversity 248 of grassland avian species. Although this is not surprising this result is important for aiding urban 249 planning practices that may need to conserve various bird species with different habitat 250 preferences. 251 252 The observation that generalist avian species diversity positively correlates with landscape 253 fragmentation also suggest that generalist bird species benefit from forest loss and fragmentation. 254 This is consistent with previous studies from Central Europe [e.g., 21, 35, 43] and North 255 America [e.g., 6, 18, 31, 44] which link the behavioral traits of generalist avian species to 256 ubiquitous opportunities presented by intensely built-up landscapes. 257 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 7

258 Overall, this study provides evidence that high resolution satellite imagery offer improved 259 opportunities for estimating the effect of urban development on biodiversity in particular avian 260 species diversity. The best model explained 79% variation in avian species diversity. This 261 coefficient of determination is higher than obtained by Coops et al. [12] and Guo et al. [10] across 262 various spatiotemporal scales in temperate landscapes. Coops et al. [12] used a number of 263 vegetation indices derived from MODIS to predict breeding bird species richness in Ontario, 264 Canada and their highest coefficient of determination (R2) was 75%. Guo et al. [10] and Wood et 265 al. [11] on the other hand found weak to average relationships between landscape spectral 266 vegetation indices and avian species diversity derived from a coarse Air photo and Landsat 267 Thematic Mapper (TM) to estimate avian species habitat relationships in temperate landscapes of 268 Saskatchewan, Canada and Wisconsin, USA respectively and their highest coefficient of 269 determination (R2) was 54%. 270 271 This study differs from previous studies in three main ways. Firstly, studies that have used 272 vegetation to estimate avian species diversity in the temperate regions have used medium to low 273 spatial resolution imagery data such as Aerial, Landsat and MODIS images. These factors have 274 resulted in weak relationships, high errors and uncertainties. However, our study estimated avian 275 species diversity from landscape metrics derived from high spatial resolution satellite imagery 276 with very low error margins. Thus, it is important to note that integrating landscape metrics 277 derived from high spatial resolution satellite imagery improved avian species prediction 278 compared to previous studies. Secondly, there is paucity in studies conducted in tropical 279 ecosystems that relate landscape metrics to avian species diversity in urban landscapes yet avian 280 species diversity is a biodiversity indicator that has important insights to the science of urban 281 environmental change. Finally, unlike previous studies that only determined the relationships 282 between vegetation indices and avian species diversity we quantified landscape structure 283 attributes in terms of size, shape and isolation distance at a fine spatial scale. Again, we find this 284 especially important in African tropical urban landscapes where tree cover is low, much of the 285 built-up areas have no tarmac cover and much of the urban development is informal and poorly 286 planned, thus making high spatial resolution satellite imagery an excellent alternative to 287 delineating spatial variability habitat fragmentation. However, it will be useful to test the 288 applicability of these models in independent study sites to observe whether the form of remotely 289 sensed models of landscape metrics are consistent and can be improved further. Nevertheless, we 290 make a claim that this finding provides an opportunity to quantifying the impact of urban 291 landscape pattern on biodiversity in tropical urban landscapes of sub-Saharan Africa. 292 293 Conclusion 294 The main objective of this study was to test whether and to what extent avian species respond to 295 constraints including habitat fragment size, shape complexity and isolation distance in urbanizing 296 tropical ecosystems. From the results of this study, we conclude that the: 297 1. size, shape and isolation distance of habitat fragments matter to woodland specialist avian 298 species; 299 2. shape of habitat fragments matter to grassland specialist species, than isolation and size 300 of grassland fragments; and 301 3. the increasing complexity of habitat fragment shape and size increases the diversity of 302 generalist species than isolation. 303 We therefore conclude that urban planning can improve biodiversity in urban landscapes by 304 managing the size, shape and isolation distance of habitat fragments. Such approaches to urban bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 8

305 development can create conditions suitable for avian species persistence in urban landscapes. 306 Large, regular shaped and interconnected habitat fragments are also fundamental to the 307 conservation of avian species in urban landscapes. Future urban development strategies should 308 therefore consider habitat conditions necessary for species persistence, by managing the size, 309 shape and isolation distance of undeveloped grassland and forested areas in urban ecosystems. 310 We suggest further studies that aim to assess the variation of avian species diversity in relation to 311 land use, primary productivity, climatic and topographic variables to assess the pattern of the 312 distribution and assess whether or not further improvements for estimating biodiversity impacts 313 of urban development can be achieved. 314 315 Acknowledgements 316 We are grateful to Anna Zivumbwa for support in the field.

317 Author Contributions 318 Conceived and designed the study: AM, PT, TM, and NC. Collected the data: TM, PT, NC 319 Analyzed the data: TM. Wrote the manuscript: TM with contributions from AM, PT and NC. 320 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 9

322 References

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414 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 11

416 Tables

417 Table 1. Study sites sampling design matrix and definitions of land cover/land use types Land Cover/Use Subcategory Description Transects Type Low urbanization 1 Grasslands in Low Density Residential 5 grasslands Areas 2 Grasslands in High Density Residential 5 Areas Low urbanization 3 Forested areas in Low Density Residential 5 forested areas Areas 4 Forested areas in High Density Residential 5 Areas Built-up area: 5 Low Density Residential Areas 5 6 High Density Residential Areas 5 7 Central Business Districts and Industrial 5 Areas 418 419

420 Table 2. Summary statistics of bird species observed by landscape type Menhinick’s Berger-Parker Simpson's Abundance Richness Land use/cover class Index Index Index Low urbanization forested 1286.00 25.00±36.00 2.45±2.88 0.78±0.86 0.90±0.93 Low urbanization grasslands 1980.00 28.87±11.21 1.64±0.34 0.14±0.82 0.80±0.90 Built-up Area 2815.00 18.13±11.68 1.60±2.29 0.70±0.15 0.95±0.06 421

422

423 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 12

425 Figure captions

426 Figure 1. Map of the study area showing the 35 bird observation sites georeferenced in WGS84 427 and the coordinates are in Decimal Degrees

428 Figure 2. Relationship between woodland specialist avian species diversity (Menhinick’s, 429 Berger-Parker’s Indexes) and landscape structure (a = size, b= shape, c = isolation distance) 430

431 Figure 3. Relationship between grassland specialist avian species diversity and landscape 432 structure (shape) 433

434 Figure 4. Relationship between generalist avian species diversity and landscape structure (a= 435 size, b= shape) 436 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 13

438 Supplementary information 439 440 SS 1: Descriptive statistics of the patch metrics by land use/land cover type

Patch metrics Forested Grasslands Built-up area Size Metrics Min Max Mean SD Min Max Mean SD Min Max Mean SD Meff CUT 0.01 1.70 0.61 0.58 0.05 0.63 0.33 0.21 0.00 0.21 0.10 0.07 Meff CBC 0.14 15.58 4.38 5.17 0.06 4.98 1.07 1.45 0.10 0.21 0.25 0.35 PLAND 0.23 0.91 0.66 0.18 12.83 20.04 17.92 2.65 1.57 11.63 6.31 3.79 NUMB 4.00 127.00 42.30 40.06 9.00 226.00 67.4 2.65 108.00 400.00 288.33 96.81 MPS 2.54 197.20 68.63 67.80 2.49 86.49 26.27 25.11 0.20 4.31 0.23 1.28 MEDPS 0.38 1.76 0.83 0.39 0.15 1.20 0.38 0.32 0.13 0.45 0.30 0.09 PSCOV 171.9 835.84 446.05 197.66 278.24 785.23 552.5 160.95 110.12 770.14 369.09 242.05 PSSD 8.5 638.14 227.39 200.91 17.03 240.64 113.8 69.26 0.22 33.19 0.37 10.25 Shape Metrics ED 0.02 0.12 0.05 0.03 0.02 0.12 0.05 0.03 0.11 0.33 0.20 0.06 TE 19.88 117.6 45.65 33.69 16.4 66.88 34.64 15.57 20.52 73.33 42.45 17.18 MPE 0.29 4.97 2.25 1.82 0.26 1.82 0.89 0.55 0.07 0.50 0.23 0.15 MSI 1.73 2.73 2.09 0.34 1.56 2.26 1.84 0.22 1.39 1.91 1.64 0.15 MPAR 0.17 0.35 0.25 0.06 0.18 0.44 0.35 0.08 0.27 0.45 0.34 0.05 MSPFD 1.49 1.61 1.55 0.04 1.49 1.67 1.61 0.05 1.56 1.66 1.6 0.03 AWMSI 3.44 18.68 8.82 4.74 4.17 14.39 7.17 2.91 1.52 12.54 5.02 4.31 AWMPFD 1.45 1.57 1.51 0.04 1.42 1.59 1.50 0.05 1.53 1.65 1.58 0.04 Isolation Metrics PI 0.67 2.38 1.22 0.51 0.69 1.38 0.90 0.21 0.76 1.32 1.08 0.17 OMD 35.8 265.32 126.33 77.44 32.79 95.48 62.7 22.88 39.59 119.35 74.22 24.1 441 442 Note that values for PSSD, MEDPS, MPS, MPE and TE were scaled down by a factor of 1,000.Where: Effective Mesh Size CUT 443 (Meff CUT), Effective Mesh Size CBC (Meff CBC), Percent Landscape Area (PLAND), Number of Patches (NUMB), Mean 444 Patch Size (MPS), Median Patch Size (MEDPS), Patch Size Coefficient of Variation (PSCOV), Patch Size Standard Deviation 445 (PSSD), Edge Density (ED), Total Edge (TE), Mean Patch Edge (MPE), Mean Shape Index (MSI), Mean Perimeter Area Ratio 446 (MPAR), Mean Shape Patch Fractal Dimension (MSPFD), Area Weighted Mean Shape Index (AWMSI), Area Weighted Mean 447 Patch Fractal Dimension (AWMPFD), Proximity Index (PI), Observed Mean Distance (OMD). 448 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 14

450 SS2: List of avian species recorded in the Harare Metropolitan Region Common Name Scientific Name Count Yellow-throated Sparrow Petronia superciliaris 25 House Sparrow Passer domesticus 220 Grey-headed Sparrow Passer griseus 29 Terrestrial Bulbul Phyllastrephus terrestris 3 Black-eyed Bulbul Pycnonotus nigricans 227 Tropical Boubou aethiopicus 21 White-bellied Sunbird Nectarinia talatala 24 Yellow-bellied (Variable) Sunbird Nectarinia venutsa 3 Miombo double-collared sunbird Nectarinia manoensis 22 Black Sunbird Nectarinia amethystina 51 Scarlet-chested Sunbird Nectarinia senegalensis 13 Cardinal Woodpecker Dendropicos fuscescens 3 Golden-tailed Woodpecker Campethera abingoni 7 Grey Lourie Corythaixoides concolor 8 Purple-crested Lourie Tauraco porphyreolophus 32 Green-spotted Dove Turtur chalcospilos 2 Cape Turtle Dove Streptopelia capicola 32 Laughing Dove streptopelia senegalensis 238 Red-eyed Dove Streptopelia semitorquata 109 Rock Pigeon Columbra guinea 10 Feral Pigeons Columba livia 238 Crested Barbet Trachyphonus viallantii 17 Whyte's Barbet Stactolaema whytii 8 Yellow-fronted Tinker Barbet Pogoniuslus chrysoconus 7 Black-collared Barbet Lybius torquatus 9 Long-billed Crombec Sylveitta rufescens 1 Striped Kingfisher Halcyon chelicuti 4 Greater Blue-eared Starling Lamprotornis chalybaeus 11 Red-winged Starling Onychognathus morio 65 Plum-coloured Starling Cinnyricinclus leucogaster 3 Arrow-marked Babbler Turdoides jardineii 31 Lanner Falcon Falco biarmicus 4 Peregrine Falcon Falco peregrinus 1 Eastern Red-footed Falcon Falco amurensis 101 Fiery-necked Nightjar Caprimulgus pectoralis 1 Southern Black Tit Parus niger 3 Buzzard Kaupifalco monogrammicus 1 Southern Black Flycatcher Melaenornis pammelaina 12 Paradise Flycatcher Terpsiphone viridis 21 Spotted Flycatcher Muscicapa striata 2 Red-billed Woodhoopoe Pheoniculus purpureus 11 Puffback Dryoscopus cubla 39 Chinspot Batis Batis molitor 3 Fork-tailed Drongo Dicrurus adsimilis 48 Greater Honeyguide Indicator 4 Kurrichane Turdus libonyana 26 Golden-breasted Bunting Embreriza flaviventris 1 Rock Bunting Embreriza tahapisi 2 Brubru Nilaus afer 6 Yellow White-eye Zosterops senegalensis 60 Black-headed Oriole Oriolus larvatus 9 Melba Finch Pytilia melba 2 Gabar Goshawk Micronisus gabar 1 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 15

Cuckoo Hawk Aviceda cuculoides 1 Little Banded Goshawk Accipiter badius 2 Black Sparrowhawk Accipiter melanoleucus 3 Little Sparrowhawk Accipiter minullus 6 White Helmetshrike Prionops plumatus 28 Fiscal Lanius collaris 2 Paradise Whydah Vidua paradisaea 1 Pin-tailed Whydah Vidua macroura 23 Red-headed Weaver Anaplectes rubriceps 1 Golden Weaver Ploceus xanthrops 4 Spectacled Weaver Ploceus ocularis 6 Southern Masked Weaver Ploceus velatus 83 Thick-billed Weaver Amblyospiza albifrons 8 Little Bee-eater Merops pusillus 5 European Bee-eater Merops apiaster 79 Grey Hornbil Tockus nasutus 1 Streaky-headed Canary Serinus gularis 23 Yellow-eyed Canary Serinus mozambicus 55 Black-eared Canary Serinus mennelli 12 Tawnyflanked Prinia Prinia subflava 41 Black-breasted Eagle Circaetus gallicus 3 African Fish Eagle Haliaeetus vocifer 2 Long-crested Eagle Lophaetus occipitalis 3 Wahlberg's Eagle Aquila wahlbergi 3 Eastern Saw-wing Swallow Psalidoprocne orientalis 1 European Swallow Hirundo rustica 231 Grey-rumped Swallow Pseudhirundo griseopyga 11 Red-breasted Swallow Hirundo semirufa 3 Wire-tailed Swallow Hirundo smithii 8 Palm Swift Cypsiurus parvus 391 Little Swift Apus affinis 393 Neddicky Cisticola fulvicapilla 1 Wood Owl Strix woodfordii 2 Marsh Owl Asio capensis 2 Grey-backed Bleating Warbler Camaroptera brevicaudata 8 Willow Warbler Phylloscopus trochilus 25 Great Reed Warbler Acrocephalus arundinaceus 1 Centropus senegalensis 2 Hueglin's Robin Cossypha heuglini 11 Kurrichane Buttonquail Turnix sylvatica 6 Reed Cormorant Phalacrocorax africanus 1 Black-crowned Tchagra Tchagra senegala 5 Hamerkop Scopus umbretta 1 Jameson's Firefinch Lagonosticta rhodopareia 17 Cattle Egret Bubulcus ibis 10 Bronze Mannikin Spermestes cuculatus 145 Common Waxbill Estrilda astrild 43 Orange-breasted Waxbill Sporaeginthus subflavus 22 Blue Waxbill Uraeginthus anglolensis 38 Red Bishop Euplectes orix 365 Yellow-backed Widow Euplectes macrourus 57 Red-collared Widow Euplectes ardens 18 Yellow-rumped Widow Euplectes capensis 15 Red-billed Quelea Quelea 1631 Swainson's Francolin Francolinus afer 12 Stonechat Saxicola torquata 5 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 16

Helmeted Guineafowl Numida meleagris 8 Chirping Cistcola Cisticola pipiens 7 Croaking Cisticola Cisticola natalensis 1 Levaillant's Cisticola Cisticola tinniens 11 Rattling Cisticolla Cisticola chiniana 15 Rufous-naped Lark Mirafra africana 1 Steelblue Widowfinch Vidua chalybeata 20 Yellow-throated Longclaw Macronyx croceus 8 Pink-throated Longclaw Macronyx ameliae 2 Black-headed Heron Ardea melanocephala 15 Sacred Ibis Threskiornis aethiopicus 11 Black-shouldered Kite Elanus caeruleus 4 Speckled Mousebird Colius striatus 12 Pied Crow Corvus albus 227 Yellow wagtail Motacilla flava 3 Jacobin Cuckoo Clamator jacobinus 1 Diederik Cuckoo Chrysococcyx caprius 1 Black Campephaga flava 1 African Cuckoo Cuculus gularis 1 Common Sandpiper Tringa hypoleucos 1 Abdim Stock Ciconia abdimii 25 451 452 453 454 455 Possible Reviewers 456 1. Seth Mago, Director, Urban Wildlife Institute, Lincoln Park Zoo, email 457 [email protected] 458 2. Wendy McWilliam, Senior Lecturer, Lincoln University, NZ, email 459 [email protected] 460 3. Amanda D Rodwald, Professor; Director of Conservation Science, Cornell Lab of 461 Ornithology, [email protected]

462 bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/388702; this version posted August 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.